Abstract：As the missing information in the image is increasing, the existing methods extracting information from only a single image can not produce satisfactory completion results. Therefore, an automatic label conditional generative adversarial network(CGAN) based on image semantic is presented from the perspective of multi-granular cognition. It can be applied on image denoising and image completion. Firstly, the multi-layer semantic information from unlabeled images based on the Gaussian cloud transform algorithm is extracted. Then, the original images are segmented and the segmented images are labeled automatically in accordance with different granular semantic information. Furthermore, different granular segmented images and their labels are used as the training samples in the CGAN to get an image probability generation model, respectively. The large missing regions from a single image are completed based on the similar image generated by cloud semantic and CGAN. On the datasets of Caltech-UCSD Birds and Oxford-102flowers, the proposed model achieves the high performance in image denoising and image completion.
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